Long noncoding RNAs emerge from transposon-derived antisense sequences and may contribute to infection stage-specific transposon regulation in a fungal phytopathogen.

BACKGROUND: The genome of the obligate biotrophic phytopathogenic barley powdery mildew fungus Blumeria hordei is inflated due to highly abundant and possibly active transposable elements (TEs). In the absence of the otherwise common repeat-induced point mutation transposon defense mechanism, noncoding RNAs could be key for regulating the activity of TEs and coding genes during the pathogenic life cycle. RESULTS: We performed time-course whole-transcriptome shotgun sequencing (RNA-seq) of total RNA derived from infected barley leaf epidermis at various stages of fungal pathogenesis and observed significant transcript accumulation and time point-dependent regulation of TEs in B. hordei. Using a manually curated consensus database of 344 TEs, we discovered phased small RNAs mapping to 104 consensus transposons, suggesting that RNA interference contributes significantly to their regulation. Further, we identified 5,127 long noncoding RNAs (lncRNAs) genome-wide in B. hordei, of which 823 originated from the antisense strand of a TE. Co-expression network analysis of lncRNAs, TEs, and coding genes throughout the asexual life cycle of B. hordei points at extensive positive and negative co-regulation of lncRNAs, subsets of TEs and coding genes. CONCLUSIONS: Our work suggests that similar to mammals and plants, fungal lncRNAs support the dynamic modulation of transcript levels, including TEs, during pivotal stages of host infection. The lncRNAs may support transcriptional diversity and plasticity amid loss of coding genes in powdery mildew fungi and may give rise to novel regulatory elements and virulence peptides, thus representing key drivers of rapid evolutionary adaptation to promote pathogenicity and overcome host defense.

CimpleG: finding simple CpG methylation signatures.

DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.